The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
As used herein, “time series” refers to a mapping from timestamps to data values. The data values in a time series are measured and/or recorded at date-time points that are represented by the timestamps. A “market instrument” (or just “instrument”) refers to a tradable element that has some kind of value. For example, any stocks and bonds and derivatives thereof (e.g. stock options, bond futures) may be represented as instruments that can be traded on stock markets and/or exchanges.
A user may be interested in many different types of time series of a particular market instrument. For example, a market analyst may be interested in seeing a chart that plots a time series comprising the closing price of the stock of Microsoft Corporation (ticker “MSFT”). Since the financial community is becoming more and more interested in analyzing a wider variety of asset classes and economy sectors, there is a growing need for analysis tools that are capable of generating different types of time series for a single market instrument as well as for combinations of market instruments. Some examples of such different types of time series include, without limitation, time series of the opening price, closing price, implied volatility (IVOL), historical volatility (HVOL) volume, market capitalization, relative strength index (RSI), dividend yield, 52-week low-high range, price-per-share to earnings-per-share (P/E) ratio and other valuation ratios, various profitability margins, and per-share earnings of market instruments.
However, the approaches used in currently available analysis tools lack the flexibility in generating time series and do not provide for interactively receiving user-specified parameters that determine the time series of interest to the user. For example, past approaches for generating time series typically provide for generating and displaying to a user only a particular type of time series (e.g. time series of the closing price of a given stock) without receiving any user input that defines the particular type of the time series or that determines the manner in which the time series is generated.
Another disadvantage of past approaches for generating time series is that a user is required to remember all options and parameters that are used in generating each particular type of time series. This may pose a serious problem to users that need to perform complex analysis based on multiple different types of time series for a large number of market instruments.